Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations707875
Missing cells2515685
Missing cells (%)22.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.4 MiB
Average record size in memory128.0 B

Variable types

Categorical2
DateTime1
Numeric13

Alerts

AQI is highly overall correlated with PM10 and 1 other fieldsHigh correlation
Benzene is highly overall correlated with Toluene and 1 other fieldsHigh correlation
NO is highly overall correlated with NOxHigh correlation
NO2 is highly overall correlated with NOx and 1 other fieldsHigh correlation
NOx is highly overall correlated with NO and 2 other fieldsHigh correlation
PM10 is highly overall correlated with AQI and 3 other fieldsHigh correlation
PM2.5 is highly overall correlated with AQI and 1 other fieldsHigh correlation
Toluene is highly overall correlated with Benzene and 1 other fieldsHigh correlation
Xylene is highly overall correlated with Benzene and 1 other fieldsHigh correlation
PM2.5 has 145088 (20.5%) missing values Missing
PM10 has 296737 (41.9%) missing values Missing
NO has 116632 (16.5%) missing values Missing
NO2 has 117122 (16.5%) missing values Missing
NOx has 123224 (17.4%) missing values Missing
NH3 has 272542 (38.5%) missing values Missing
CO has 86517 (12.2%) missing values Missing
SO2 has 130373 (18.4%) missing values Missing
O3 has 129208 (18.3%) missing values Missing
Benzene has 163646 (23.1%) missing values Missing
Toluene has 220607 (31.2%) missing values Missing
Xylene has 455829 (64.4%) missing values Missing
AQI has 129080 (18.2%) missing values Missing
AQI_Bucket has 129080 (18.2%) missing values Missing
CO is highly skewed (γ1 = 20.85295945) Skewed
Benzene is highly skewed (γ1 = 22.51160265) Skewed
NOx has 19811 (2.8%) zeros Zeros
CO has 66088 (9.3%) zeros Zeros
Benzene has 118727 (16.8%) zeros Zeros
Toluene has 89705 (12.7%) zeros Zeros
Xylene has 49350 (7.0%) zeros Zeros

Reproduction

Analysis started2024-12-30 23:46:35.342403
Analysis finished2024-12-30 23:47:30.462311
Duration55.12 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

City
Categorical

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 MiB
Ahmedabad
48192 
Bengaluru
48192 
Chennai
48192 
Mumbai
48192 
Lucknow
48192 
Other values (21)
466915 

Length

Max length18
Median length12
Mean length8.2754724
Min length5

Characters and Unicode

Total characters5858000
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAhmedabad
2nd rowAhmedabad
3rd rowAhmedabad
4th rowAhmedabad
5th rowAhmedabad

Common Values

ValueCountFrequency (%)
Ahmedabad 48192
 
6.8%
Bengaluru 48192
 
6.8%
Chennai 48192
 
6.8%
Mumbai 48192
 
6.8%
Lucknow 48192
 
6.8%
Delhi 48192
 
6.8%
Hyderabad 48107
 
6.8%
Patna 44554
 
6.3%
Gurugram 40258
 
5.7%
Visakhapatnam 35053
 
5.0%
Other values (16) 250751
35.4%

Length

2024-12-30T17:47:30.581669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ahmedabad 48192
 
6.8%
bengaluru 48192
 
6.8%
chennai 48192
 
6.8%
mumbai 48192
 
6.8%
lucknow 48192
 
6.8%
delhi 48192
 
6.8%
hyderabad 48107
 
6.8%
patna 44554
 
6.3%
gurugram 40258
 
5.7%
visakhapatnam 35053
 
5.0%
Other values (16) 250751
35.4%

Most occurring characters

ValueCountFrequency (%)
a 1109817
18.9%
r 504103
 
8.6%
u 369145
 
6.3%
n 366662
 
6.3%
h 327804
 
5.6%
i 327468
 
5.6%
e 272265
 
4.6%
m 263480
 
4.5%
d 199861
 
3.4%
t 199045
 
3.4%
Other values (28) 1918350
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5858000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1109817
18.9%
r 504103
 
8.6%
u 369145
 
6.3%
n 366662
 
6.3%
h 327804
 
5.6%
i 327468
 
5.6%
e 272265
 
4.6%
m 263480
 
4.5%
d 199861
 
3.4%
t 199045
 
3.4%
Other values (28) 1918350
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5858000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1109817
18.9%
r 504103
 
8.6%
u 369145
 
6.3%
n 366662
 
6.3%
h 327804
 
5.6%
i 327468
 
5.6%
e 272265
 
4.6%
m 263480
 
4.5%
d 199861
 
3.4%
t 199045
 
3.4%
Other values (28) 1918350
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5858000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1109817
18.9%
r 504103
 
8.6%
u 369145
 
6.3%
n 366662
 
6.3%
h 327804
 
5.6%
i 327468
 
5.6%
e 272265
 
4.6%
m 263480
 
4.5%
d 199861
 
3.4%
t 199045
 
3.4%
Other values (28) 1918350
32.7%
Distinct48192
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size5.4 MiB
Minimum2015-01-01 01:00:00
Maximum2020-07-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2024-12-30T17:47:30.752930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:30.919002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PM2.5
Real number (ℝ)

High correlation  Missing 

Distinct34105
Distinct (%)6.1%
Missing145088
Missing (%)20.5%
Infinite0
Infinite (%)0.0%
Mean67.622994
Minimum0.01
Maximum999.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 MiB
2024-12-30T17:47:31.126031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile10.5
Q126.2
median46.42
Q379.49
95-th percentile201.08
Maximum999.99
Range999.98
Interquartile range (IQR)53.29

Descriptive statistics

Standard deviation74.730496
Coefficient of variation (CV)1.1051048
Kurtosis32.710409
Mean67.622994
Median Absolute Deviation (MAD)23.96
Skewness4.3051943
Sum38057342
Variance5584.647
MonotonicityNot monotonic
2024-12-30T17:47:31.346128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 1679
 
0.2%
11 1050
 
0.1%
27 936
 
0.1%
15 929
 
0.1%
24 885
 
0.1%
21 875
 
0.1%
28 853
 
0.1%
17 852
 
0.1%
29 842
 
0.1%
22 841
 
0.1%
Other values (34095) 553045
78.1%
(Missing) 145088
 
20.5%
ValueCountFrequency (%)
0.01 6
 
< 0.1%
0.02 14
< 0.1%
0.03 11
< 0.1%
0.04 14
< 0.1%
0.05 11
< 0.1%
0.06 6
 
< 0.1%
0.07 15
< 0.1%
0.08 9
< 0.1%
0.09 17
< 0.1%
0.1 16
< 0.1%
ValueCountFrequency (%)
999.99 193
< 0.1%
997.93 1
 
< 0.1%
996.97 1
 
< 0.1%
995 75
 
< 0.1%
994.78 1
 
< 0.1%
994.58 1
 
< 0.1%
994 1
 
< 0.1%
992.16 1
 
< 0.1%
991.2 1
 
< 0.1%
986 1
 
< 0.1%

PM10
Real number (ℝ)

High correlation  Missing 

Distinct45327
Distinct (%)11.0%
Missing296737
Missing (%)41.9%
Infinite0
Infinite (%)0.0%
Mean119.0758
Minimum0.01
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 MiB
2024-12-30T17:47:31.517270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile21.53
Q152.38
median91.5
Q3147.52
95-th percentile326.64
Maximum1000
Range999.99
Interquartile range (IQR)95.14

Descriptive statistics

Standard deviation104.22475
Coefficient of variation (CV)0.87528069
Kurtosis9.6406278
Mean119.0758
Median Absolute Deviation (MAD)44.26
Skewness2.5086698
Sum48956588
Variance10862.799
MonotonicityNot monotonic
2024-12-30T17:47:31.667380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94 3432
 
0.5%
38 407
 
0.1%
36 382
 
0.1%
27 373
 
0.1%
44 369
 
0.1%
48 359
 
0.1%
31 359
 
0.1%
40 357
 
0.1%
42 342
 
< 0.1%
59 342
 
< 0.1%
Other values (45317) 404416
57.1%
(Missing) 296737
41.9%
ValueCountFrequency (%)
0.01 15
< 0.1%
0.02 19
< 0.1%
0.03 28
< 0.1%
0.04 24
< 0.1%
0.05 14
 
< 0.1%
0.06 23
< 0.1%
0.07 18
< 0.1%
0.08 18
< 0.1%
0.09 17
< 0.1%
0.1 37
< 0.1%
ValueCountFrequency (%)
1000 57
< 0.1%
999.99 41
< 0.1%
999.75 1
 
< 0.1%
998.19 1
 
< 0.1%
998.1 1
 
< 0.1%
997.26 1
 
< 0.1%
995.23 1
 
< 0.1%
993.1 1
 
< 0.1%
991.07 1
 
< 0.1%
991.02 1
 
< 0.1%

NO
Real number (ℝ)

High correlation  Missing 

Distinct19887
Distinct (%)3.4%
Missing116632
Missing (%)16.5%
Infinite0
Infinite (%)0.0%
Mean17.421755
Minimum0.01
Maximum499.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 MiB
2024-12-30T17:47:31.836097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile1.14
Q13.84
median7.96
Q316.15
95-th percentile69.12
Maximum499.99
Range499.98
Interquartile range (IQR)12.31

Descriptive statistics

Standard deviation32.095211
Coefficient of variation (CV)1.842249
Kurtosis41.136902
Mean17.421755
Median Absolute Deviation (MAD)4.9
Skewness5.3847201
Sum10300491
Variance1030.1025
MonotonicityNot monotonic
2024-12-30T17:47:32.002800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 977
 
0.1%
3 931
 
0.1%
2.9 842
 
0.1%
2.8 836
 
0.1%
3.1 831
 
0.1%
2.1 786
 
0.1%
1.8 784
 
0.1%
1.5 783
 
0.1%
2.6 774
 
0.1%
2.85 762
 
0.1%
Other values (19877) 582937
82.4%
(Missing) 116632
 
16.5%
ValueCountFrequency (%)
0.01 69
 
< 0.1%
0.02 91
< 0.1%
0.03 85
< 0.1%
0.04 57
 
< 0.1%
0.05 55
 
< 0.1%
0.06 60
 
< 0.1%
0.07 80
< 0.1%
0.08 72
< 0.1%
0.09 62
 
< 0.1%
0.1 178
< 0.1%
ValueCountFrequency (%)
499.99 1
< 0.1%
499.52 1
< 0.1%
498.97 1
< 0.1%
498.57 1
< 0.1%
497.4 1
< 0.1%
496.23 1
< 0.1%
495.87 1
< 0.1%
494.9 1
< 0.1%
494.43 1
< 0.1%
494.09 1
< 0.1%

NO2
Real number (ℝ)

High correlation  Missing 

Distinct18115
Distinct (%)3.1%
Missing117122
Missing (%)16.5%
Infinite0
Infinite (%)0.0%
Mean28.885157
Minimum0.01
Maximum499.51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 MiB
2024-12-30T17:47:32.156155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile3.73
Q110.81
median20.32
Q336.35
95-th percentile81.94
Maximum499.51
Range499.5
Interquartile range (IQR)25.54

Descriptive statistics

Standard deviation29.162194
Coefficient of variation (CV)1.009591
Kurtosis20.679073
Mean28.885157
Median Absolute Deviation (MAD)10.93
Skewness3.3431675
Sum17063993
Variance850.43359
MonotonicityNot monotonic
2024-12-30T17:47:32.310022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.5 532
 
0.1%
7 374
 
0.1%
10 364
 
0.1%
10.12 355
 
0.1%
9.2 335
 
< 0.1%
9.88 334
 
< 0.1%
9.12 328
 
< 0.1%
8.8 325
 
< 0.1%
9 324
 
< 0.1%
10.18 316
 
< 0.1%
Other values (18105) 587166
82.9%
(Missing) 117122
 
16.5%
ValueCountFrequency (%)
0.01 50
 
< 0.1%
0.02 193
< 0.1%
0.03 252
< 0.1%
0.04 192
< 0.1%
0.05 170
< 0.1%
0.06 181
< 0.1%
0.07 178
< 0.1%
0.08 168
< 0.1%
0.09 130
< 0.1%
0.1 199
< 0.1%
ValueCountFrequency (%)
499.51 1
< 0.1%
495.56 1
< 0.1%
494.15 1
< 0.1%
493.42 1
< 0.1%
485.46 1
< 0.1%
485.16 1
< 0.1%
482.15 1
< 0.1%
478.23 1
< 0.1%
478.2 1
< 0.1%
477.37 1
< 0.1%

NOx
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct23602
Distinct (%)4.0%
Missing123224
Missing (%)17.4%
Infinite0
Infinite (%)0.0%
Mean32.287565
Minimum0
Maximum498.61
Zeros19811
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size5.4 MiB
2024-12-30T17:47:32.460063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.58
Q110.66
median20.79
Q337.15
95-th percentile105.59
Maximum498.61
Range498.61
Interquartile range (IQR)26.49

Descriptive statistics

Standard deviation39.756669
Coefficient of variation (CV)1.2313307
Kurtosis20.452373
Mean32.287565
Median Absolute Deviation (MAD)12
Skewness3.6879741
Sum18876957
Variance1580.5927
MonotonicityNot monotonic
2024-12-30T17:47:32.621290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19811
 
2.8%
4.22 9936
 
1.4%
6.24 3440
 
0.5%
2.21 1495
 
0.2%
5 832
 
0.1%
4.9 724
 
0.1%
6.9 375
 
0.1%
7 344
 
< 0.1%
9 332
 
< 0.1%
8.9 322
 
< 0.1%
Other values (23592) 547040
77.3%
(Missing) 123224
 
17.4%
ValueCountFrequency (%)
0 19811
2.8%
0.01 29
 
< 0.1%
0.02 47
 
< 0.1%
0.03 105
 
< 0.1%
0.04 48
 
< 0.1%
0.05 65
 
< 0.1%
0.06 15
 
< 0.1%
0.07 30
 
< 0.1%
0.08 35
 
< 0.1%
0.09 20
 
< 0.1%
ValueCountFrequency (%)
498.61 1
< 0.1%
498.29 1
< 0.1%
496.29 1
< 0.1%
496 1
< 0.1%
494.73 1
< 0.1%
494.54 1
< 0.1%
493.73 1
< 0.1%
493.4 1
< 0.1%
493.13 1
< 0.1%
492.84 1
< 0.1%

NH3
Real number (ℝ)

Missing 

Distinct16426
Distinct (%)3.8%
Missing272542
Missing (%)38.5%
Infinite0
Infinite (%)0.0%
Mean23.607959
Minimum0.01
Maximum499.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 MiB
2024-12-30T17:47:32.794085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile2.57
Q18.12
median15.38
Q329.23
95-th percentile65.63
Maximum499.97
Range499.96
Interquartile range (IQR)21.11

Descriptive statistics

Standard deviation28.8319
Coefficient of variation (CV)1.2212788
Kurtosis47.56148
Mean23.607959
Median Absolute Deviation (MAD)8.92
Skewness5.2440197
Sum10277323
Variance831.27846
MonotonicityNot monotonic
2024-12-30T17:47:33.014339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 868
 
0.1%
2.5 511
 
0.1%
5 449
 
0.1%
6.5 436
 
0.1%
6.38 431
 
0.1%
6.25 416
 
0.1%
6.33 404
 
0.1%
6.4 387
 
0.1%
6.62 384
 
0.1%
6.6 377
 
0.1%
Other values (16416) 430670
60.8%
(Missing) 272542
38.5%
ValueCountFrequency (%)
0.01 71
 
< 0.1%
0.02 64
 
< 0.1%
0.03 93
< 0.1%
0.04 39
 
< 0.1%
0.05 113
< 0.1%
0.06 31
 
< 0.1%
0.07 29
 
< 0.1%
0.08 105
< 0.1%
0.09 13
 
< 0.1%
0.1 205
< 0.1%
ValueCountFrequency (%)
499.97 1
< 0.1%
499.56 1
< 0.1%
499.12 1
< 0.1%
498.54 1
< 0.1%
497.99 1
< 0.1%
497.95 1
< 0.1%
497.88 1
< 0.1%
497.38 1
< 0.1%
495.23 1
< 0.1%
493.6 1
< 0.1%

CO
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct7017
Distinct (%)1.1%
Missing86517
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean2.1835393
Minimum0
Maximum498.57
Zeros66088
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size5.4 MiB
2024-12-30T17:47:33.184966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.42
median0.8
Q31.37
95-th percentile6.18
Maximum498.57
Range498.57
Interquartile range (IQR)0.95

Descriptive statistics

Standard deviation10.970514
Coefficient of variation (CV)5.0241892
Kurtosis586.31918
Mean2.1835393
Median Absolute Deviation (MAD)0.44
Skewness20.852959
Sum1356759.6
Variance120.35219
MonotonicityNot monotonic
2024-12-30T17:47:33.347518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 66088
 
9.3%
0.62 4650
 
0.7%
0.64 4635
 
0.7%
0.7 4595
 
0.6%
0.52 4574
 
0.6%
0.66 4530
 
0.6%
0.68 4522
 
0.6%
0.6 4507
 
0.6%
0.48 4445
 
0.6%
0.72 4445
 
0.6%
Other values (7007) 514367
72.7%
(Missing) 86517
 
12.2%
ValueCountFrequency (%)
0 66088
9.3%
0.01 905
 
0.1%
0.02 553
 
0.1%
0.03 599
 
0.1%
0.04 680
 
0.1%
0.05 826
 
0.1%
0.06 702
 
0.1%
0.07 636
 
0.1%
0.08 995
 
0.1%
0.09 669
 
0.1%
ValueCountFrequency (%)
498.57 1
< 0.1%
494.9 1
< 0.1%
490.35 1
< 0.1%
485.73 1
< 0.1%
483.37 1
< 0.1%
476.2 1
< 0.1%
476.02 1
< 0.1%
475.4 1
< 0.1%
473.81 1
< 0.1%
470.72 1
< 0.1%

SO2
Real number (ℝ)

Missing 

Distinct14145
Distinct (%)2.4%
Missing130373
Missing (%)18.4%
Infinite0
Infinite (%)0.0%
Mean14.038307
Minimum0.01
Maximum199.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 MiB
2024-12-30T17:47:33.570931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile1.83
Q14.88
median8.37
Q314.78
95-th percentile45.62
Maximum199.96
Range199.95
Interquartile range (IQR)9.9

Descriptive statistics

Standard deviation19.30554
Coefficient of variation (CV)1.3752042
Kurtosis26.105521
Mean14.038307
Median Absolute Deviation (MAD)4.27
Skewness4.4367951
Sum8107150.6
Variance372.70386
MonotonicityNot monotonic
2024-12-30T17:47:33.823787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.5 679
 
0.1%
5.4 664
 
0.1%
4 651
 
0.1%
5 651
 
0.1%
5.62 648
 
0.1%
6 641
 
0.1%
4.5 639
 
0.1%
6.3 624
 
0.1%
6.1 616
 
0.1%
4.38 612
 
0.1%
Other values (14135) 571077
80.7%
(Missing) 130373
 
18.4%
ValueCountFrequency (%)
0.01 49
 
< 0.1%
0.02 110
 
< 0.1%
0.03 102
 
< 0.1%
0.04 81
 
< 0.1%
0.05 79
 
< 0.1%
0.06 76
 
< 0.1%
0.07 63
 
< 0.1%
0.08 76
 
< 0.1%
0.09 66
 
< 0.1%
0.1 378
0.1%
ValueCountFrequency (%)
199.96 2
< 0.1%
199.95 1
< 0.1%
199.93 1
< 0.1%
199.85 1
< 0.1%
199.81 1
< 0.1%
199.77 1
< 0.1%
199.75 1
< 0.1%
199.72 1
< 0.1%
199.7 1
< 0.1%
199.65 1
< 0.1%

O3
Real number (ℝ)

Missing 

Distinct16909
Distinct (%)2.9%
Missing129208
Missing (%)18.3%
Infinite0
Infinite (%)0.0%
Mean34.798979
Minimum0.01
Maximum497.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 MiB
2024-12-30T17:47:34.077713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile3.79
Q113.42
median26.24
Q347.62
95-th percentile94.65
Maximum497.62
Range497.61
Interquartile range (IQR)34.2

Descriptive statistics

Standard deviation29.806379
Coefficient of variation (CV)0.85653027
Kurtosis4.3715225
Mean34.798979
Median Absolute Deviation (MAD)15.41
Skewness1.6935193
Sum20137021
Variance888.42023
MonotonicityNot monotonic
2024-12-30T17:47:34.272049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.48 424
 
0.1%
17 404
 
0.1%
22.14 373
 
0.1%
3 347
 
< 0.1%
43.02 334
 
< 0.1%
43.77 323
 
< 0.1%
9.8 291
 
< 0.1%
0.1 290
 
< 0.1%
7.8 263
 
< 0.1%
19.6 257
 
< 0.1%
Other values (16899) 575361
81.3%
(Missing) 129208
 
18.3%
ValueCountFrequency (%)
0.01 48
 
< 0.1%
0.02 97
 
< 0.1%
0.03 62
 
< 0.1%
0.04 54
 
< 0.1%
0.05 55
 
< 0.1%
0.06 42
 
< 0.1%
0.07 52
 
< 0.1%
0.08 43
 
< 0.1%
0.09 43
 
< 0.1%
0.1 290
< 0.1%
ValueCountFrequency (%)
497.62 1
< 0.1%
474.58 1
< 0.1%
474.2 1
< 0.1%
471.12 1
< 0.1%
440 1
< 0.1%
424.42 1
< 0.1%
419.71 1
< 0.1%
388.79 1
< 0.1%
382.92 1
< 0.1%
368.08 1
< 0.1%

Benzene
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct6073
Distinct (%)1.1%
Missing163646
Missing (%)23.1%
Infinite0
Infinite (%)0.0%
Mean3.087595
Minimum0
Maximum498.07
Zeros118727
Zeros (%)16.8%
Negative0
Negative (%)0.0%
Memory size5.4 MiB
2024-12-30T17:47:34.469402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.05
median0.86
Q32.75
95-th percentile9.49
Maximum498.07
Range498.07
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation16.456599
Coefficient of variation (CV)5.3299085
Kurtosis573.29287
Mean3.087595
Median Absolute Deviation (MAD)0.86
Skewness22.511603
Sum1680358.7
Variance270.81964
MonotonicityNot monotonic
2024-12-30T17:47:34.674232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 118727
 
16.8%
0.1 7408
 
1.0%
0.03 4442
 
0.6%
0.35 3699
 
0.5%
0.2 3697
 
0.5%
0.04 3567
 
0.5%
0.01 3502
 
0.5%
0.05 3461
 
0.5%
0.02 3356
 
0.5%
0.08 3038
 
0.4%
Other values (6063) 389332
55.0%
(Missing) 163646
23.1%
ValueCountFrequency (%)
0 118727
16.8%
0.01 3502
 
0.5%
0.02 3356
 
0.5%
0.03 4442
 
0.6%
0.04 3567
 
0.5%
0.05 3461
 
0.5%
0.06 2108
 
0.3%
0.07 2211
 
0.3%
0.08 3038
 
0.4%
0.09 1958
 
0.3%
ValueCountFrequency (%)
498.07 4
< 0.1%
491.51 8
< 0.1%
488.48 1
 
< 0.1%
487.79 1
 
< 0.1%
487.21 1
 
< 0.1%
487.2 1
 
< 0.1%
486.58 1
 
< 0.1%
485.69 1
 
< 0.1%
485.44 2
 
< 0.1%
485.21 2
 
< 0.1%

Toluene
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct10383
Distinct (%)2.1%
Missing220607
Missing (%)31.2%
Infinite0
Infinite (%)0.0%
Mean8.6609266
Minimum0
Maximum499.4
Zeros89705
Zeros (%)12.7%
Negative0
Negative (%)0.0%
Memory size5.4 MiB
2024-12-30T17:47:34.869350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.37
median2.59
Q38.41
95-th percentile35.21
Maximum499.4
Range499.4
Interquartile range (IQR)8.04

Descriptive statistics

Standard deviation21.741023
Coefficient of variation (CV)2.5102422
Kurtosis200.96317
Mean8.6609266
Median Absolute Deviation (MAD)2.59
Skewness11.494659
Sum4220192.4
Variance472.6721
MonotonicityNot monotonic
2024-12-30T17:47:35.049245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 89705
 
12.7%
1.1 2589
 
0.4%
0.1 2452
 
0.3%
0.2 1899
 
0.3%
0.3 1315
 
0.2%
0.03 1233
 
0.2%
80.04 1155
 
0.2%
0.5 1145
 
0.2%
0.08 1133
 
0.2%
0.18 1122
 
0.2%
Other values (10373) 383520
54.2%
(Missing) 220607
31.2%
ValueCountFrequency (%)
0 89705
12.7%
0.01 418
 
0.1%
0.02 561
 
0.1%
0.03 1233
 
0.2%
0.04 760
 
0.1%
0.05 942
 
0.1%
0.06 781
 
0.1%
0.07 960
 
0.1%
0.08 1133
 
0.2%
0.09 559
 
0.1%
ValueCountFrequency (%)
499.4 1
 
< 0.1%
498.07 4
< 0.1%
491.6 1
 
< 0.1%
491.51 5
< 0.1%
488.53 1
 
< 0.1%
488.48 1
 
< 0.1%
487.79 1
 
< 0.1%
487.21 1
 
< 0.1%
487.2 1
 
< 0.1%
486.58 1
 
< 0.1%

Xylene
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct4340
Distinct (%)1.7%
Missing455829
Missing (%)64.4%
Infinite0
Infinite (%)0.0%
Mean3.1305368
Minimum0
Maximum499.99
Zeros49350
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size5.4 MiB
2024-12-30T17:47:35.872676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1
median0.79
Q33.12
95-th percentile13.32
Maximum499.99
Range499.99
Interquartile range (IQR)3.02

Descriptive statistics

Standard deviation7.8348316
Coefficient of variation (CV)2.5027119
Kurtosis419.4058
Mean3.1305368
Median Absolute Deviation (MAD)0.79
Skewness13.693196
Sum789039.28
Variance61.384586
MonotonicityNot monotonic
2024-12-30T17:47:36.116834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 49350
 
7.0%
2 5962
 
0.8%
0.1 5478
 
0.8%
0.65 4119
 
0.6%
3.21 2653
 
0.4%
0.52 1940
 
0.3%
0.08 1935
 
0.3%
0.2 1694
 
0.2%
0.12 1634
 
0.2%
0.05 1579
 
0.2%
Other values (4330) 175702
 
24.8%
(Missing) 455829
64.4%
ValueCountFrequency (%)
0 49350
7.0%
0.01 1042
 
0.1%
0.02 1471
 
0.2%
0.03 1432
 
0.2%
0.04 1147
 
0.2%
0.05 1579
 
0.2%
0.06 1452
 
0.2%
0.07 1502
 
0.2%
0.08 1935
 
0.3%
0.09 1490
 
0.2%
ValueCountFrequency (%)
499.99 1
< 0.1%
461.39 1
< 0.1%
433.94 1
< 0.1%
402.64 1
< 0.1%
396.06 1
< 0.1%
347.1 1
< 0.1%
327.16 1
< 0.1%
319.95 1
< 0.1%
278.69 1
< 0.1%
277.18 1
< 0.1%

AQI
Real number (ℝ)

High correlation  Missing 

Distinct1556
Distinct (%)0.3%
Missing129080
Missing (%)18.2%
Infinite0
Infinite (%)0.0%
Mean166.4135
Minimum8
Maximum3133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 MiB
2024-12-30T17:47:36.314684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile46
Q179
median116
Q3208
95-th percentile397
Maximum3133
Range3125
Interquartile range (IQR)129

Descriptive statistics

Standard deviation162.11273
Coefficient of variation (CV)0.97415611
Kurtosis75.439063
Mean166.4135
Median Absolute Deviation (MAD)49
Skewness6.3813666
Sum96319302
Variance26280.537
MonotonicityNot monotonic
2024-12-30T17:47:36.518291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102 5371
 
0.8%
68 5021
 
0.7%
104 4984
 
0.7%
78 4928
 
0.7%
106 4848
 
0.7%
72 4817
 
0.7%
66 4813
 
0.7%
100 4812
 
0.7%
80 4775
 
0.7%
70 4736
 
0.7%
Other values (1546) 529690
74.8%
(Missing) 129080
 
18.2%
ValueCountFrequency (%)
8 1
 
< 0.1%
9 8
 
< 0.1%
10 21
 
< 0.1%
11 53
< 0.1%
12 41
< 0.1%
13 35
< 0.1%
14 29
< 0.1%
15 49
< 0.1%
16 54
< 0.1%
17 54
< 0.1%
ValueCountFrequency (%)
3133 8
< 0.1%
3111 8
< 0.1%
3084 8
< 0.1%
3057 8
< 0.1%
3043 8
< 0.1%
3001 8
< 0.1%
3000 8
< 0.1%
2996 8
< 0.1%
2987 8
< 0.1%
2969 8
< 0.1%

AQI_Bucket
Categorical

Missing 

Distinct6
Distinct (%)< 0.1%
Missing129080
Missing (%)18.2%
Memory size5.4 MiB
Moderate
198991 
Satisfactory
189434 
Poor
66654 
Very Poor
57455 
Good
38611 

Length

Max length12
Median length9
Mean length8.5854076
Min length4

Characters and Unicode

Total characters4969191
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPoor
2nd rowModerate
3rd rowModerate
4th rowModerate
5th rowModerate

Common Values

ValueCountFrequency (%)
Moderate 198991
28.1%
Satisfactory 189434
26.8%
Poor 66654
 
9.4%
Very Poor 57455
 
8.1%
Good 38611
 
5.5%
Severe 27650
 
3.9%
(Missing) 129080
18.2%

Length

2024-12-30T17:47:36.749504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T17:47:36.968422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
moderate 198991
31.3%
satisfactory 189434
29.8%
poor 124109
19.5%
very 57455
 
9.0%
good 38611
 
6.1%
severe 27650
 
4.3%

Most occurring characters

ValueCountFrequency (%)
o 713865
14.4%
r 597639
12.0%
t 577859
11.6%
a 577859
11.6%
e 538387
10.8%
y 246889
 
5.0%
d 237602
 
4.8%
S 217084
 
4.4%
M 198991
 
4.0%
i 189434
 
3.8%
Other values (8) 873582
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4969191
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 713865
14.4%
r 597639
12.0%
t 577859
11.6%
a 577859
11.6%
e 538387
10.8%
y 246889
 
5.0%
d 237602
 
4.8%
S 217084
 
4.4%
M 198991
 
4.0%
i 189434
 
3.8%
Other values (8) 873582
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4969191
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 713865
14.4%
r 597639
12.0%
t 577859
11.6%
a 577859
11.6%
e 538387
10.8%
y 246889
 
5.0%
d 237602
 
4.8%
S 217084
 
4.4%
M 198991
 
4.0%
i 189434
 
3.8%
Other values (8) 873582
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4969191
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 713865
14.4%
r 597639
12.0%
t 577859
11.6%
a 577859
11.6%
e 538387
10.8%
y 246889
 
5.0%
d 237602
 
4.8%
S 217084
 
4.4%
M 198991
 
4.0%
i 189434
 
3.8%
Other values (8) 873582
17.6%

Interactions

2024-12-30T17:47:23.723563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:53.852396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:56.141348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:59.348516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:02.196490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:04.662261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:07.483315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:09.692640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:12.469552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:14.900522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:17.475549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:19.762418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:21.922226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:23.920376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:53.991821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:56.288105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:59.553247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:02.374753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:04.818454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:07.646869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:09.885552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:12.637815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:15.054873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:17.626930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:19.911572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:22.048208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:24.101946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:54.171327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:56.482695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:59.835057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:02.583020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:05.019238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:07.816615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:10.081374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:12.831564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:15.231565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:17.786855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:20.092368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:22.177255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:24.290557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:54.325704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:56.885329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:00.168028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:02.781450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:05.206272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:07.999090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:10.279884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:13.033308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:15.451732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:17.973099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:20.257474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:22.319191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:24.483382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:54.522809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:57.216941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:00.487059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:02.984765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:05.383469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:08.155599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:10.473749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:13.230086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:15.676831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:18.156196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:20.429804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:22.464256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:24.717587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:54.668301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:57.515954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:00.653624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:03.167546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:05.542570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:08.332288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:10.652496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:13.408990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:15.848754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:18.328407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:20.649682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:22.577040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:24.903348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:55.194941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:57.839772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:00.848427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:03.354939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:05.880133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:08.520448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:10.852742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:13.605733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:16.093078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:18.509697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:20.825722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:22.712626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:25.098202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:55.350228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:58.048023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:01.047861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:03.545001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:06.132308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:08.703659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:11.045326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:13.800821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:16.334821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:18.713116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:20.991050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:22.866241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:25.301038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:55.493021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:58.221039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:01.247896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:03.774201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:06.435213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:08.865607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:11.264225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:14.009499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:16.512224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:18.898580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:21.176367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:23.008463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:25.474612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:55.625218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:58.437981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:01.441510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:03.951603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:06.646794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:09.025815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:11.478223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:14.218500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:16.678591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:19.072256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:21.345212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:23.130800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:25.643320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:55.748717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:58.652662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:01.634903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:04.149870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:06.843897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:09.184430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:11.660365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:14.392726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:16.849423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:19.250784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:21.489084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:23.266728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:25.804603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:55.853435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:58.848332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:01.808583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:04.288446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:07.084141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:09.307025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:11.797202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:14.526075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:16.974733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:19.390001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:21.623547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:23.394970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:25.997610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:56.003301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:46:59.133507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:02.010901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:04.482230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:07.290139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:09.485740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:11.981428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:14.710472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:17.214659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:19.561260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:21.797565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-30T17:47:23.532411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-12-30T17:47:37.148815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AQIAQI_BucketBenzeneCOCityNH3NONO2NOxO3PM10PM2.5SO2TolueneXylene
AQI1.0000.4280.2590.4560.1890.3550.3510.4180.4090.1600.7820.7530.3230.3510.125
AQI_Bucket0.4281.0000.0350.1440.3430.1240.1330.1810.1720.1190.3930.3400.1560.0990.034
Benzene0.2590.0351.0000.2910.0940.1370.2410.3550.2960.0540.3480.2560.1550.7900.721
CO0.4560.1440.2911.0000.1000.2100.3400.3020.356-0.0640.3140.3470.2670.4020.391
City0.1890.3430.0940.1001.0000.1670.1160.1400.1350.0900.1910.1500.2280.1170.026
NH30.3550.1240.1370.2100.1671.0000.3160.4710.3390.1270.3710.3650.1030.116-0.120
NO0.3510.1330.2410.3400.1160.3161.0000.4230.725-0.2450.4230.3400.2660.2180.250
NO20.4180.1810.3550.3020.1400.4710.4231.0000.6700.0840.5020.4350.2250.4140.238
NOx0.4090.1720.2960.3560.1350.3390.7250.6701.000-0.1340.5020.3930.2840.3210.253
O30.1600.1190.054-0.0640.0900.127-0.2450.084-0.1341.0000.1270.0870.1050.094-0.045
PM100.7820.3930.3480.3140.1910.3710.4230.5020.5020.1271.0000.8570.3430.4090.168
PM2.50.7530.3400.2560.3470.1500.3650.3400.4350.3930.0870.8571.0000.2410.2920.201
SO20.3230.1560.1550.2670.2280.1030.2660.2250.2840.1050.3430.2411.0000.2950.260
Toluene0.3510.0990.7900.4020.1170.1160.2180.4140.3210.0940.4090.2920.2951.0000.620
Xylene0.1250.0340.7210.3910.026-0.1200.2500.2380.253-0.0450.1680.2010.2600.6201.000

Missing values

2024-12-30T17:47:26.521589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-30T17:47:27.425947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-30T17:47:29.593007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CityDatetimePM2.5PM10NONO2NOxNH3COSO2O3BenzeneTolueneXyleneAQIAQI_Bucket
0Ahmedabad2015-01-01 01:00:00NaNNaN1.0040.0136.37NaN1.00122.07NaN0.00.00.0NaNNaN
1Ahmedabad2015-01-01 02:00:00NaNNaN0.0227.7519.73NaN0.0285.90NaN0.00.00.0NaNNaN
2Ahmedabad2015-01-01 03:00:00NaNNaN0.0819.3211.08NaN0.0852.83NaN0.00.00.0NaNNaN
3Ahmedabad2015-01-01 04:00:00NaNNaN0.3016.459.20NaN0.3039.53153.580.00.00.0NaNNaN
4Ahmedabad2015-01-01 05:00:00NaNNaN0.1214.907.85NaN0.1232.63NaN0.00.00.0NaNNaN
5Ahmedabad2015-01-01 06:00:00NaNNaN0.3315.9510.82NaN0.3329.8764.250.00.00.0NaNNaN
6Ahmedabad2015-01-01 07:00:00NaNNaN0.4515.9412.47NaN0.4527.41191.960.00.00.0NaNNaN
7Ahmedabad2015-01-01 08:00:00NaNNaN1.0316.6616.48NaN1.0320.92177.210.00.00.0NaNNaN
8Ahmedabad2015-01-01 09:00:00NaNNaN1.4716.2518.02NaN1.4716.45122.080.00.00.0NaNNaN
9Ahmedabad2015-01-01 10:00:00NaNNaN2.0513.7816.08NaN2.0515.14NaN0.00.00.0NaNNaN
CityDatetimePM2.5PM10NONO2NOxNH3COSO2O3BenzeneTolueneXyleneAQIAQI_Bucket
707865Visakhapatnam2020-06-30 15:00:0015.7530.250.9513.758.074.670.302.9041.550.00.00.051.0Satisfactory
707866Visakhapatnam2020-06-30 16:00:0014.0029.501.7720.7012.458.080.457.1239.230.00.00.051.0Satisfactory
707867Visakhapatnam2020-06-30 17:00:009.7524.252.8823.8515.0212.230.578.4534.780.00.00.051.0Satisfactory
707868Visakhapatnam2020-06-30 18:00:006.5024.752.2523.3813.6810.600.665.8033.55NaNNaNNaN51.0Satisfactory
707869Visakhapatnam2020-06-30 19:00:008.2533.250.3524.0513.055.550.441.8541.38NaNNaNNaN51.0Satisfactory
707870Visakhapatnam2020-06-30 20:00:009.5036.002.7525.5715.854.570.62NaN27.75NaNNaNNaN51.0Satisfactory
707871Visakhapatnam2020-06-30 21:00:0017.2549.253.6233.2020.623.780.762.0225.58NaNNaNNaN51.0Satisfactory
707872Visakhapatnam2020-06-30 22:00:0036.0071.002.2030.8018.203.670.581.7726.15NaNNaNNaN50.0Good
707873Visakhapatnam2020-06-30 23:00:0015.7563.001.0228.9016.003.800.490.7515.82NaNNaNNaN50.0Good
707874Visakhapatnam2020-07-01 00:00:0015.0066.000.4026.8514.055.200.592.1017.05NaNNaNNaN50.0Good